In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

published:23 Mar 2013

views:1613

Wysii by TellMePlus
The predictive, contextual and behavioral targeting platform for mobile

published:05 Apr 2014

views:640

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

published:23 Mar 2013

views:149

published:02 Oct 2014

views:27

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.

published:30 Jun 2015

views:169

Making a Recommendation Engine at ParallelDots
a. Why normal full-text search will not work: The problem of incorrect tagging and slow search queries.
b. ParallelDots’ MVP with TopicModels: Issues with accuracy and scaling.
c. Decision to use Deep Learning and aims of the new architecture (Not enough funds for distributed system, search related posts from millions of documents in reasonable time)
Basics of Deep Learning
a. DeepNeural Networks
b. Types of Deep Neural Networks. Convolutional, DBNs, Recurrent and Recursive. How do they differ in structure, types of neurons and training.
c. Backpropogation and its variants
d. Features of various Deep Learning libraries in Python.
Deep Learning in NLP
a. Solving problem of high-dimensionality using word embeddings.
b. Common approaches to word embedding.
c. Modelling language as a series of characters using Recurrent Neural Networks .
c. Models we use : Named Entity Recognition with Neural Nets
d. Models we use: Combining word embeddings using heuristics and recursive neural networks.
Search Engine
a. Using SearchData Structures to convert search related posts operations from O(n) to O(log(n))
b. Space Partitioning Trees : Search for nearest Neighbours. Examples of such trees: KD-Tree / BallTree / VP Tree
c. Why we chose VP Tree ? What libraries to use to code up in Python ?
d. Parallelization. DataParallel Python’s multiprocessing parallelization not the best, working towards a shared memory parallel version.
Scaling up system
a. Hacks to scale up recommendations.
b. Using golang’s channels to unique requests.

Understanding Consumer Intent & Leveraging Predictive Technologies
Predictive mobile apps are growing in use with a wide variety of productivity and calendaring apps garnering consumer and press attention. What's next for predictive technologies in mobile? How can predictive technologies be used to improve customer experiences and help brands grow? How can companies tap into contextual cues, like location, calendar, email, etc. to better predict and respond to what their consumers want? What other cues can be used to better understand users and respond to their needs? Where are consumers drawing the line between cool and creepy? Join us on the main stage for a conversation with the CEOs of two white hot predictive apps companies - Max Wheeler from Mynd and Mikael Berner from EasilyDo who will be talking to Mark Daiss, the Co-Founder of the intelligent home screen, Aviate (acquired by Yahoo in January 2014) about how predictive technologies can be used to help companies grow.
Panelists: Mikael Berner, CEO & Founder, Easilydo; Max Wheeler, CEO & Founder, Mynd; Mark Daiss, Co-Founder of Aviate, Yahoo
Moderator: Molly Wood, DeputyTechnology Editor, New York TimesWant to learn more about VentureBeat events?
Visit events.venturebeat.com

Deep learning

Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervisedfeature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

Contextual Intelligence - predictive reminders

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

3:08

Wysii - Predictive contextual mobile targeting

Wysii - Predictive contextual mobile targeting

Wysii - Predictive contextual mobile targeting

Wysii by TellMePlus
The predictive, contextual and behavioral targeting platform for mobile

1:59

Grokr (contextual predictive search app) on iPad mini

Grokr (contextual predictive search app) on iPad mini

Grokr (contextual predictive search app) on iPad mini

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

Contextual Anomaly Detection in Big Sensor Data

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.

14:42

Muktabh Mayank - Making a contextual recommendation engine

Muktabh Mayank - Making a contextual recommendation engine

Muktabh Mayank - Making a contextual recommendation engine

Making a Recommendation Engine at ParallelDots
a. Why normal full-text search will not work: The problem of incorrect tagging and slow search queries.
b. ParallelDots’ MVP with TopicModels: Issues with accuracy and scaling.
c. Decision to use Deep Learning and aims of the new architecture (Not enough funds for distributed system, search related posts from millions of documents in reasonable time)
Basics of Deep Learning
a. DeepNeural Networks
b. Types of Deep Neural Networks. Convolutional, DBNs, Recurrent and Recursive. How do they differ in structure, types of neurons and training.
c. Backpropogation and its variants
d. Features of various Deep Learning libraries in Python.
Deep Learning in NLP
a. Solving problem of high-dimensionality using word embeddings.
b. Common approaches to word embedding.
c. Modelling language as a series of characters using Recurrent Neural Networks .
c. Models we use : Named Entity Recognition with Neural Nets
d. Models we use: Combining word embeddings using heuristics and recursive neural networks.
Search Engine
a. Using SearchData Structures to convert search related posts operations from O(n) to O(log(n))
b. Space Partitioning Trees : Search for nearest Neighbours. Examples of such trees: KD-Tree / BallTree / VP Tree
c. Why we chose VP Tree ? What libraries to use to code up in Python ?
d. Parallelization. DataParallel Python’s multiprocessing parallelization not the best, working towards a shared memory parallel version.
Scaling up system
a. Hacks to scale up recommendations.
b. Using golang’s channels to unique requests.

Understanding Consumer Intent & Leveraging Predictive Technologies
Predictive mobile apps are growing in use with a wide variety of productivity and calendaring apps garnering consumer and press attention. What's next for predictive technologies in mobile? How can predictive technologies be used to improve customer experiences and help brands grow? How can companies tap into contextual cues, like location, calendar, email, etc. to better predict and respond to what their consumers want? What other cues can be used to better understand users and respond to their needs? Where are consumers drawing the line between cool and creepy? Join us on the main stage for a conversation with the CEOs of two white hot predictive apps companies - Max Wheeler from Mynd and Mikael Berner from EasilyDo who will be talking to Mark Daiss, the Co-Founder of the intelligent home screen, Aviate (acquired by Yahoo in January 2014) about how predictive technologies can be used to help companies grow.
Panelists: Mikael Berner, CEO & Founder, Easilydo; Max Wheeler, CEO & Founder, Mynd; Mark Daiss, Co-Founder of Aviate, Yahoo
Moderator: Molly Wood, DeputyTechnology Editor, New York TimesWant to learn more about VentureBeat events?
Visit events.venturebeat.com

2:06

Contextual Insights: Making Better Decisions

Contextual Insights: Making Better Decisions

Contextual Insights: Making Better Decisions

When you need to make an important decision, you want the information that will help you make the best choice. And you want that information in context and at your fingertips. Check out this product spotlight to see how Workday gives you what you need to make better decisions.
Learn more about the EnterpriseCloud for HR and Finance in our ProductPreview at - https://forms.workday.com/uk/landing_page/product_preview_uk_enterprise_cloud_for_hr_and_finance_lp.php

6:01

Why contextual real-time communications is a huge telco opportunity

Why contextual real-time communications is a huge telco opportunity

Why contextual real-time communications is a huge telco opportunity

Almost any app you look at will now have some sort of real-time communication function built in contextually, says David Walsh, who explains why he thinks this is the biggest opportunity for the carriers to develop services as they transform their infrastructure with NFV - now really taking off - and look to become fast-moving service marketing companies, rather than slow-moving engineering organisations. The old legacy world is changing fast and everything, including Genband’s own software, is being reworked and loaded into the cloud. This will enable telcos to become fast-moving, fast fail, marketing and sales organisations.
Featuring David Walsh, President, CEO and Chairman, GENBAND
FILMED AT: Mobile World Congress 2017, Barcelona

Matti Aksela, VP of analytics at Comptel Corporation, walks through Comptel's new concept of Contextual Intelligence for Telecommunications (CIQ4T) that helps take customer experience management to the next level and enables CSPs to improve their business performance. Utilising advanced predictive analytics, Matti explains the company's innovative approach for allowing CSPs to better determine customers' needs, wants, likes and dislikes at a granular level based on historical and real-time data and predictive modelling.

9:32

Mercedes Benz Contextual Car Capabilities

Mercedes Benz Contextual Car Capabilities

Mercedes Benz Contextual Car Capabilities

I visited Mercedes Benz' advanced R&D lab in Silicon Valley on Friday and got a look at what Mercedes is thinking of when it comes to a contextual car that will study your usage of the car and help you get to where you need to go.
Is this interesting to you? Or is it too freaky?

2:30

Importance of Contextual Intelligence

Importance of Contextual Intelligence

Importance of Contextual Intelligence

3:01

Predictive Analytics in Healthcare

Predictive Analytics in Healthcare

Predictive Analytics in Healthcare

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning
Objectives:
► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols
► Promotion of wellness and disease management
The Predictors – Predictive Analytics:
In order to predict the re-admission, following data fields/predictors were considered.
► Demographics – Age, Sex
► Lab data – Includes lab tests
► Vitals – Includes BP, Sugar, Weight, etc.
► Visit types – Emergency, In-patient, and Outpatient
► Diagnosis – Diseases/ailments – Heart, Pneumonia
► Previous hospital visit
► Length of stay
► The data was received as a set of .csv files which gave the complete details of Demographics, Admission, vitals, lab tests of selected sample of patients over a period of time.
► The processing of the data included the following activities:
► Removing commas, uploading .csv files to HDFS (Horton works)
► The required DDL scripts were written in Hive
► The necessary joins were written
► The result was refined datasets
► The refined datasets are passed on to Data Analysis team for analysis
The best model is arrived at by testing the data under different classifiers and precision, recall and F1 score metrics calculated for each classifier.
► Gradient Boosting
► Random Forest
► Support Vector Machines
► Logistic Regression
► K-Nearest Neighbor
► Ridge
_________________________________________________________________
Like the Video follow us for more:
Facebook: https://www.facebook.com/altencalsoftlabs
Twitter: https://twitter.com/altencalsoftlab
LinkedIn: https://www.linkedin.com/company/calsoft-labs-india-p-ltd-an-alten-group-company
Google+: https://plus.google.com/+Altencalsoftlabs/
_________________________________________________________________
Looking for similar IT Services?
Write to us Business@Altencalsoftlabs.com
(OR)
Visit Us @ http://www.altencalsoftlabs.com/

19:28

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find container which is already full. The result is a huge amount of waste buried or burned where it could have been recycled.
A contextual predictive model was developed in order to provide the citizens with the information: what is the best moment to go to which recycling center in terms of waiting time and bin availability ?
This predictive model depends on sensors deployed in each recycling centers and various open data sources.
The API here is the web that links avery parts:
- the sensors to push new version of the software and source the measurements in real time
- the predictive models is fed with fresh measurements and fresh data
- the web/mobile app with the predictions
- the users demands are crowdsourced to a server
- the BI tools of the waste managements authorities
We hope to demonstrate how a predictive API like ours can solve real life problems.
### The slides for this presentation can be found at http://lanyrd.com/2014/papis2014/sdfyrz/
### For more content like this, sign up to the PAPIs.io newsletter at http://papis.io/#updates

3:33

Selligent Success Story: Contextual Marketing for Redfin

Selligent Success Story: Contextual Marketing for Redfin

Selligent Success Story: Contextual Marketing for Redfin

Learn how Lisa Taylor, Director of Customer Marketing, from Redfin uses weather and other contextual data to engage their customers in the moment.

3:28

The Evolution of Telematics Predictive Modeling

The Evolution of Telematics Predictive Modeling

The Evolution of Telematics Predictive Modeling

Telematics models have become much more predictive, now incorporating contextual behavior. WatchDavid Lukens of LexisNexis explain the evolution of where it started and where telematics modeling is today.

4:19

Visteon Contextual UX Cockpit

Visteon Contextual UX Cockpit

Visteon Contextual UX Cockpit

Visteon's Contextual UX (User Experience) Cockpit brings the latest HMI input technologies together to deliver a unique user experience through predictive options based on contextual and historical user data to reduce or eliminate menu structure options. This technology features a low-cost Infrared LED spatial gesture interaction; a Time of Flight camera to enable touch gestures on the instrument panel; pressure-sensitive touch pad that enables 3-D touch features and always listening, contextual voice commands. The contextual UX cockpit pit shows how Visteon can use its low-cost, flexible vehicle simulator to prove out in-vehicle experiences before implementing into a vehicle.

0:50

Amazing Contextual Search in Email: kikin for Android

Amazing Contextual Search in Email: kikin for Android

Amazing Contextual Search in Email: kikin for Android

kikin Inc., headquartered in SoHo, New YorkCity, was founded by successful serial entrepreneurs with a passion for compelling, disruptive consumer products. We are a group of talented professionals who strive to improve everyone's access to information.
kikin is empowering people to learn and do more via contextual search. Select anything of interest in an application or website to obtain highly relevant search results. On touch devices, kikin eliminates extraneous typing or cutting-and-pasting with "Touch to Search"; "long press" on words or phrases to receive highly relevant answers.
To learn more about kikin please visit us at www.kikin.com.

Contextual Intelligence - predictive reminders

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

published: 23 Mar 2013

Wysii - Predictive contextual mobile targeting

Wysii by TellMePlus
The predictive, contextual and behavioral targeting platform for mobile

published: 05 Apr 2014

Grokr (contextual predictive search app) on iPad mini

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

Contextual Anomaly Detection in Big Sensor Data

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detecti...

published: 30 Jun 2015

Muktabh Mayank - Making a contextual recommendation engine

Making a Recommendation Engine at ParallelDots
a. Why normal full-text search will not work: The problem of incorrect tagging and slow search queries.
b. ParallelDots’ MVP with TopicModels: Issues with accuracy and scaling.
c. Decision to use Deep Learning and aims of the new architecture (Not enough funds for distributed system, search related posts from millions of documents in reasonable time)
Basics of Deep Learning
a. DeepNeural Networks
b. Types of Deep Neural Networks. Convolutional, DBNs, Recurrent and Recursive. How do they differ in structure, types of neurons and training.
c. Backpropogation and its variants
d. Features of various Deep Learning libraries in Python.
Deep Learning in NLP
a. Solving problem of high-dimensionality using word embeddings.
b. Common approache...

Predictive Intelligence: The Marriage of Data and Analytics in Cybersecurity

Understanding Consumer Intent & Leveraging Predictive Technologies
Predictive mobile apps are growing in use with a wide variety of productivity and calendaring apps garnering consumer and press attention. What's next for predictive technologies in mobile? How can predictive technologies be used to improve customer experiences and help brands grow? How can companies tap into contextual cues, like location, calendar, email, etc. to better predict and respond to what their consumers want? What other cues can be used to better understand users and respond to their needs? Where are consumers drawing the line between cool and creepy? Join us on the main stage for a conversation with the CEOs of two white hot predictive apps companies - Max Wheeler from Mynd and Mikael Berner from EasilyDo who ...

published: 18 Apr 2014

Contextual Insights: Making Better Decisions

When you need to make an important decision, you want the information that will help you make the best choice. And you want that information in context and at your fingertips. Check out this product spotlight to see how Workday gives you what you need to make better decisions.
Learn more about the EnterpriseCloud for HR and Finance in our ProductPreview at - https://forms.workday.com/uk/landing_page/product_preview_uk_enterprise_cloud_for_hr_and_finance_lp.php

published: 24 Mar 2015

Why contextual real-time communications is a huge telco opportunity

Almost any app you look at will now have some sort of real-time communication function built in contextually, says David Walsh, who explains why he thinks this is the biggest opportunity for the carriers to develop services as they transform their infrastructure with NFV - now really taking off - and look to become fast-moving service marketing companies, rather than slow-moving engineering organisations. The old legacy world is changing fast and everything, including Genband’s own software, is being reworked and loaded into the cloud. This will enable telcos to become fast-moving, fast fail, marketing and sales organisations.
Featuring David Walsh, President, CEO and Chairman, GENBAND
FILMED AT: Mobile World Congress 2017, Barcelona

Matti Aksela, VP of analytics at Comptel Corporation, walks through Comptel's new concept of Contextual Intelligence for Telecommunications (CIQ4T) that helps take customer experience management to the next level and enables CSPs to improve their business performance. Utilising advanced predictive analytics, Matti explains the company's innovative approach for allowing CSPs to better determine customers' needs, wants, likes and dislikes at a granular level based on historical and real-time data and predictive modelling.

published: 27 Jan 2014

Mercedes Benz Contextual Car Capabilities

I visited Mercedes Benz' advanced R&D lab in Silicon Valley on Friday and got a look at what Mercedes is thinking of when it comes to a contextual car that will study your usage of the car and help you get to where you need to go.
Is this interesting to you? Or is it too freaky?

published: 27 Apr 2014

Importance of Contextual Intelligence

published: 20 Feb 2014

Predictive Analytics in Healthcare

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning
Objectives:
► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols
► Promotion of wellness and disease management
The Predictors – Predictive Analytics:
In order to predict the re-admission, following data fie...

published: 21 Jul 2016

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find container which is already full. The result is a huge amount of waste buried or burned where it could have been recycled.
A contextual predictive model was developed in order to provide the citizens with the information: what is the best moment to go to which recycling center in terms of waiting time and bin availability ?
This predictive model depends on sensors deployed in each recycling centers and various open data sources.
The API here is the web that links avery parts:
- the sensors to push new version of the software and source the measurements in real time
- the predictive models is fed with fresh measurements and fresh ...

published: 08 Jan 2015

Selligent Success Story: Contextual Marketing for Redfin

Learn how Lisa Taylor, Director of Customer Marketing, from Redfin uses weather and other contextual data to engage their customers in the moment.

published: 02 Dec 2016

The Evolution of Telematics Predictive Modeling

Telematics models have become much more predictive, now incorporating contextual behavior. WatchDavid Lukens of LexisNexis explain the evolution of where it started and where telematics modeling is today.

published: 24 Feb 2016

Visteon Contextual UX Cockpit

Visteon's Contextual UX (User Experience) Cockpit brings the latest HMI input technologies together to deliver a unique user experience through predictive options based on contextual and historical user data to reduce or eliminate menu structure options. This technology features a low-cost Infrared LED spatial gesture interaction; a Time of Flight camera to enable touch gestures on the instrument panel; pressure-sensitive touch pad that enables 3-D touch features and always listening, contextual voice commands. The contextual UX cockpit pit shows how Visteon can use its low-cost, flexible vehicle simulator to prove out in-vehicle experiences before implementing into a vehicle.

published: 07 Mar 2016

Amazing Contextual Search in Email: kikin for Android

kikin Inc., headquartered in SoHo, New YorkCity, was founded by successful serial entrepreneurs with a passion for compelling, disruptive consumer products. We are a group of talented professionals who strive to improve everyone's access to information.
kikin is empowering people to learn and do more via contextual search. Select anything of interest in an application or website to obtain highly relevant search results. On touch devices, kikin eliminates extraneous typing or cutting-and-pasting with "Touch to Search"; "long press" on words or phrases to receive highly relevant answers.
To learn more about kikin please visit us at www.kikin.com.

Contextual Intelligence - predictive reminders

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventio...

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings ...

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

Grokr (contextual predictive search app) on iPad mini

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみま...

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

Contextual Anomaly Detection in Big Sensor Data

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are...

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.

Understanding Consumer Intent & Leveraging Predictive Technologies
Predictive mobile apps are growing in use with a wide variety of productivity and calendarin...

Understanding Consumer Intent & Leveraging Predictive Technologies
Predictive mobile apps are growing in use with a wide variety of productivity and calendaring apps garnering consumer and press attention. What's next for predictive technologies in mobile? How can predictive technologies be used to improve customer experiences and help brands grow? How can companies tap into contextual cues, like location, calendar, email, etc. to better predict and respond to what their consumers want? What other cues can be used to better understand users and respond to their needs? Where are consumers drawing the line between cool and creepy? Join us on the main stage for a conversation with the CEOs of two white hot predictive apps companies - Max Wheeler from Mynd and Mikael Berner from EasilyDo who will be talking to Mark Daiss, the Co-Founder of the intelligent home screen, Aviate (acquired by Yahoo in January 2014) about how predictive technologies can be used to help companies grow.
Panelists: Mikael Berner, CEO & Founder, Easilydo; Max Wheeler, CEO & Founder, Mynd; Mark Daiss, Co-Founder of Aviate, Yahoo
Moderator: Molly Wood, DeputyTechnology Editor, New York TimesWant to learn more about VentureBeat events?
Visit events.venturebeat.com

Understanding Consumer Intent & Leveraging Predictive Technologies
Predictive mobile apps are growing in use with a wide variety of productivity and calendaring apps garnering consumer and press attention. What's next for predictive technologies in mobile? How can predictive technologies be used to improve customer experiences and help brands grow? How can companies tap into contextual cues, like location, calendar, email, etc. to better predict and respond to what their consumers want? What other cues can be used to better understand users and respond to their needs? Where are consumers drawing the line between cool and creepy? Join us on the main stage for a conversation with the CEOs of two white hot predictive apps companies - Max Wheeler from Mynd and Mikael Berner from EasilyDo who will be talking to Mark Daiss, the Co-Founder of the intelligent home screen, Aviate (acquired by Yahoo in January 2014) about how predictive technologies can be used to help companies grow.
Panelists: Mikael Berner, CEO & Founder, Easilydo; Max Wheeler, CEO & Founder, Mynd; Mark Daiss, Co-Founder of Aviate, Yahoo
Moderator: Molly Wood, DeputyTechnology Editor, New York TimesWant to learn more about VentureBeat events?
Visit events.venturebeat.com

Contextual Insights: Making Better Decisions

When you need to make an important decision, you want the information that will help you make the best choice. And you want that information in context and at y...

When you need to make an important decision, you want the information that will help you make the best choice. And you want that information in context and at your fingertips. Check out this product spotlight to see how Workday gives you what you need to make better decisions.
Learn more about the EnterpriseCloud for HR and Finance in our ProductPreview at - https://forms.workday.com/uk/landing_page/product_preview_uk_enterprise_cloud_for_hr_and_finance_lp.php

When you need to make an important decision, you want the information that will help you make the best choice. And you want that information in context and at your fingertips. Check out this product spotlight to see how Workday gives you what you need to make better decisions.
Learn more about the EnterpriseCloud for HR and Finance in our ProductPreview at - https://forms.workday.com/uk/landing_page/product_preview_uk_enterprise_cloud_for_hr_and_finance_lp.php

Why contextual real-time communications is a huge telco opportunity

Almost any app you look at will now have some sort of real-time communication function built in contextually, says David Walsh, who explains why he thinks this ...

Almost any app you look at will now have some sort of real-time communication function built in contextually, says David Walsh, who explains why he thinks this is the biggest opportunity for the carriers to develop services as they transform their infrastructure with NFV - now really taking off - and look to become fast-moving service marketing companies, rather than slow-moving engineering organisations. The old legacy world is changing fast and everything, including Genband’s own software, is being reworked and loaded into the cloud. This will enable telcos to become fast-moving, fast fail, marketing and sales organisations.
Featuring David Walsh, President, CEO and Chairman, GENBAND
FILMED AT: Mobile World Congress 2017, Barcelona

Almost any app you look at will now have some sort of real-time communication function built in contextually, says David Walsh, who explains why he thinks this is the biggest opportunity for the carriers to develop services as they transform their infrastructure with NFV - now really taking off - and look to become fast-moving service marketing companies, rather than slow-moving engineering organisations. The old legacy world is changing fast and everything, including Genband’s own software, is being reworked and loaded into the cloud. This will enable telcos to become fast-moving, fast fail, marketing and sales organisations.
Featuring David Walsh, President, CEO and Chairman, GENBAND
FILMED AT: Mobile World Congress 2017, Barcelona

Matti Aksela, VP of analytics at Comptel Corporation, walks through Comptel's new concept of Contextual Intelligence for Telecommunications (CIQ4T) that helps take customer experience management to the next level and enables CSPs to improve their business performance. Utilising advanced predictive analytics, Matti explains the company's innovative approach for allowing CSPs to better determine customers' needs, wants, likes and dislikes at a granular level based on historical and real-time data and predictive modelling.

Matti Aksela, VP of analytics at Comptel Corporation, walks through Comptel's new concept of Contextual Intelligence for Telecommunications (CIQ4T) that helps take customer experience management to the next level and enables CSPs to improve their business performance. Utilising advanced predictive analytics, Matti explains the company's innovative approach for allowing CSPs to better determine customers' needs, wants, likes and dislikes at a granular level based on historical and real-time data and predictive modelling.

I visited Mercedes Benz' advanced R&D lab in Silicon Valley on Friday and got a look at what Mercedes is thinking of when it comes to a contextual car that will study your usage of the car and help you get to where you need to go.
Is this interesting to you? Or is it too freaky?

I visited Mercedes Benz' advanced R&D lab in Silicon Valley on Friday and got a look at what Mercedes is thinking of when it comes to a contextual car that will study your usage of the car and help you get to where you need to go.
Is this interesting to you? Or is it too freaky?

Predictive Analytics in Healthcare

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived fro...

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning
Objectives:
► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols
► Promotion of wellness and disease management
The Predictors – Predictive Analytics:
In order to predict the re-admission, following data fields/predictors were considered.
► Demographics – Age, Sex
► Lab data – Includes lab tests
► Vitals – Includes BP, Sugar, Weight, etc.
► Visit types – Emergency, In-patient, and Outpatient
► Diagnosis – Diseases/ailments – Heart, Pneumonia
► Previous hospital visit
► Length of stay
► The data was received as a set of .csv files which gave the complete details of Demographics, Admission, vitals, lab tests of selected sample of patients over a period of time.
► The processing of the data included the following activities:
► Removing commas, uploading .csv files to HDFS (Horton works)
► The required DDL scripts were written in Hive
► The necessary joins were written
► The result was refined datasets
► The refined datasets are passed on to Data Analysis team for analysis
The best model is arrived at by testing the data under different classifiers and precision, recall and F1 score metrics calculated for each classifier.
► Gradient Boosting
► Random Forest
► Support Vector Machines
► Logistic Regression
► K-Nearest Neighbor
► Ridge
_________________________________________________________________
Like the Video follow us for more:
Facebook: https://www.facebook.com/altencalsoftlabs
Twitter: https://twitter.com/altencalsoftlab
LinkedIn: https://www.linkedin.com/company/calsoft-labs-india-p-ltd-an-alten-group-company
Google+: https://plus.google.com/+Altencalsoftlabs/
_________________________________________________________________
Looking for similar IT Services?
Write to us Business@Altencalsoftlabs.com
(OR)
Visit Us @ http://www.altencalsoftlabs.com/

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning
Objectives:
► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols
► Promotion of wellness and disease management
The Predictors – Predictive Analytics:
In order to predict the re-admission, following data fields/predictors were considered.
► Demographics – Age, Sex
► Lab data – Includes lab tests
► Vitals – Includes BP, Sugar, Weight, etc.
► Visit types – Emergency, In-patient, and Outpatient
► Diagnosis – Diseases/ailments – Heart, Pneumonia
► Previous hospital visit
► Length of stay
► The data was received as a set of .csv files which gave the complete details of Demographics, Admission, vitals, lab tests of selected sample of patients over a period of time.
► The processing of the data included the following activities:
► Removing commas, uploading .csv files to HDFS (Horton works)
► The required DDL scripts were written in Hive
► The necessary joins were written
► The result was refined datasets
► The refined datasets are passed on to Data Analysis team for analysis
The best model is arrived at by testing the data under different classifiers and precision, recall and F1 score metrics calculated for each classifier.
► Gradient Boosting
► Random Forest
► Support Vector Machines
► Logistic Regression
► K-Nearest Neighbor
► Ridge
_________________________________________________________________
Like the Video follow us for more:
Facebook: https://www.facebook.com/altencalsoftlabs
Twitter: https://twitter.com/altencalsoftlab
LinkedIn: https://www.linkedin.com/company/calsoft-labs-india-p-ltd-an-alten-group-company
Google+: https://plus.google.com/+Altencalsoftlabs/
_________________________________________________________________
Looking for similar IT Services?
Write to us Business@Altencalsoftlabs.com
(OR)
Visit Us @ http://www.altencalsoftlabs.com/

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find ...

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find container which is already full. The result is a huge amount of waste buried or burned where it could have been recycled.
A contextual predictive model was developed in order to provide the citizens with the information: what is the best moment to go to which recycling center in terms of waiting time and bin availability ?
This predictive model depends on sensors deployed in each recycling centers and various open data sources.
The API here is the web that links avery parts:
- the sensors to push new version of the software and source the measurements in real time
- the predictive models is fed with fresh measurements and fresh data
- the web/mobile app with the predictions
- the users demands are crowdsourced to a server
- the BI tools of the waste managements authorities
We hope to demonstrate how a predictive API like ours can solve real life problems.
### The slides for this presentation can be found at http://lanyrd.com/2014/papis2014/sdfyrz/
### For more content like this, sign up to the PAPIs.io newsletter at http://papis.io/#updates

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find container which is already full. The result is a huge amount of waste buried or burned where it could have been recycled.
A contextual predictive model was developed in order to provide the citizens with the information: what is the best moment to go to which recycling center in terms of waiting time and bin availability ?
This predictive model depends on sensors deployed in each recycling centers and various open data sources.
The API here is the web that links avery parts:
- the sensors to push new version of the software and source the measurements in real time
- the predictive models is fed with fresh measurements and fresh data
- the web/mobile app with the predictions
- the users demands are crowdsourced to a server
- the BI tools of the waste managements authorities
We hope to demonstrate how a predictive API like ours can solve real life problems.
### The slides for this presentation can be found at http://lanyrd.com/2014/papis2014/sdfyrz/
### For more content like this, sign up to the PAPIs.io newsletter at http://papis.io/#updates

The Evolution of Telematics Predictive Modeling

Telematics models have become much more predictive, now incorporating contextual behavior. WatchDavid Lukens of LexisNexis explain the evolution of where it s...

Telematics models have become much more predictive, now incorporating contextual behavior. WatchDavid Lukens of LexisNexis explain the evolution of where it started and where telematics modeling is today.

Telematics models have become much more predictive, now incorporating contextual behavior. WatchDavid Lukens of LexisNexis explain the evolution of where it started and where telematics modeling is today.

Visteon's Contextual UX (User Experience) Cockpit brings the latest HMI input technologies together to deliver a unique user experience through predictive options based on contextual and historical user data to reduce or eliminate menu structure options. This technology features a low-cost Infrared LED spatial gesture interaction; a Time of Flight camera to enable touch gestures on the instrument panel; pressure-sensitive touch pad that enables 3-D touch features and always listening, contextual voice commands. The contextual UX cockpit pit shows how Visteon can use its low-cost, flexible vehicle simulator to prove out in-vehicle experiences before implementing into a vehicle.

Visteon's Contextual UX (User Experience) Cockpit brings the latest HMI input technologies together to deliver a unique user experience through predictive options based on contextual and historical user data to reduce or eliminate menu structure options. This technology features a low-cost Infrared LED spatial gesture interaction; a Time of Flight camera to enable touch gestures on the instrument panel; pressure-sensitive touch pad that enables 3-D touch features and always listening, contextual voice commands. The contextual UX cockpit pit shows how Visteon can use its low-cost, flexible vehicle simulator to prove out in-vehicle experiences before implementing into a vehicle.

Amazing Contextual Search in Email: kikin for Android

kikin Inc., headquartered in SoHo, New YorkCity, was founded by successful serial entrepreneurs with a passion for compelling, disruptive consumer products. We...

kikin Inc., headquartered in SoHo, New YorkCity, was founded by successful serial entrepreneurs with a passion for compelling, disruptive consumer products. We are a group of talented professionals who strive to improve everyone's access to information.
kikin is empowering people to learn and do more via contextual search. Select anything of interest in an application or website to obtain highly relevant search results. On touch devices, kikin eliminates extraneous typing or cutting-and-pasting with "Touch to Search"; "long press" on words or phrases to receive highly relevant answers.
To learn more about kikin please visit us at www.kikin.com.

kikin Inc., headquartered in SoHo, New YorkCity, was founded by successful serial entrepreneurs with a passion for compelling, disruptive consumer products. We are a group of talented professionals who strive to improve everyone's access to information.
kikin is empowering people to learn and do more via contextual search. Select anything of interest in an application or website to obtain highly relevant search results. On touch devices, kikin eliminates extraneous typing or cutting-and-pasting with "Touch to Search"; "long press" on words or phrases to receive highly relevant answers.
To learn more about kikin please visit us at www.kikin.com.

Predictive Analytics in Healthcare

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning
Objectives:
► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols
► Promotion of wellness and disease management
The Predictors – Predictive Analytics:
In order to predict the re-admission, following data fie...

published: 21 Jul 2016

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find container which is already full. The result is a huge amount of waste buried or burned where it could have been recycled.
A contextual predictive model was developed in order to provide the citizens with the information: what is the best moment to go to which recycling center in terms of waiting time and bin availability ?
This predictive model depends on sensors deployed in each recycling centers and various open data sources.
The API here is the web that links avery parts:
- the sensors to push new version of the software and source the measurements in real time
- the predictive models is fed with fresh measurements and fresh ...

Wysii - Predictive contextual mobile targeting

Matti Aksela, VP of analytics at Comptel Corporation, walks through Comptel's new concept of Contextual Intelligence for Telecommunications (CIQ4T) that helps take customer experience management to the next level and enables CSPs to improve their business performance. Utilising advanced predictive analytics, Matti explains the company's innovative approach for allowing CSPs to better determine customers' needs, wants, likes and dislikes at a granular level based on historical and real-time data and predictive modelling.

published: 27 Jan 2014

Grokr (contextual predictive search app) on iPad mini

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

published: 23 Mar 2013

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

published: 23 Mar 2013

Contextual Intelligence - predictive reminders

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

Predictive Analytics in Healthcare

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived fro...

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning
Objectives:
► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols
► Promotion of wellness and disease management
The Predictors – Predictive Analytics:
In order to predict the re-admission, following data fields/predictors were considered.
► Demographics – Age, Sex
► Lab data – Includes lab tests
► Vitals – Includes BP, Sugar, Weight, etc.
► Visit types – Emergency, In-patient, and Outpatient
► Diagnosis – Diseases/ailments – Heart, Pneumonia
► Previous hospital visit
► Length of stay
► The data was received as a set of .csv files which gave the complete details of Demographics, Admission, vitals, lab tests of selected sample of patients over a period of time.
► The processing of the data included the following activities:
► Removing commas, uploading .csv files to HDFS (Horton works)
► The required DDL scripts were written in Hive
► The necessary joins were written
► The result was refined datasets
► The refined datasets are passed on to Data Analysis team for analysis
The best model is arrived at by testing the data under different classifiers and precision, recall and F1 score metrics calculated for each classifier.
► Gradient Boosting
► Random Forest
► Support Vector Machines
► Logistic Regression
► K-Nearest Neighbor
► Ridge
_________________________________________________________________
Like the Video follow us for more:
Facebook: https://www.facebook.com/altencalsoftlabs
Twitter: https://twitter.com/altencalsoftlab
LinkedIn: https://www.linkedin.com/company/calsoft-labs-india-p-ltd-an-alten-group-company
Google+: https://plus.google.com/+Altencalsoftlabs/
_________________________________________________________________
Looking for similar IT Services?
Write to us Business@Altencalsoftlabs.com
(OR)
Visit Us @ http://www.altencalsoftlabs.com/

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning
Objectives:
► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols
► Promotion of wellness and disease management
The Predictors – Predictive Analytics:
In order to predict the re-admission, following data fields/predictors were considered.
► Demographics – Age, Sex
► Lab data – Includes lab tests
► Vitals – Includes BP, Sugar, Weight, etc.
► Visit types – Emergency, In-patient, and Outpatient
► Diagnosis – Diseases/ailments – Heart, Pneumonia
► Previous hospital visit
► Length of stay
► The data was received as a set of .csv files which gave the complete details of Demographics, Admission, vitals, lab tests of selected sample of patients over a period of time.
► The processing of the data included the following activities:
► Removing commas, uploading .csv files to HDFS (Horton works)
► The required DDL scripts were written in Hive
► The necessary joins were written
► The result was refined datasets
► The refined datasets are passed on to Data Analysis team for analysis
The best model is arrived at by testing the data under different classifiers and precision, recall and F1 score metrics calculated for each classifier.
► Gradient Boosting
► Random Forest
► Support Vector Machines
► Logistic Regression
► K-Nearest Neighbor
► Ridge
_________________________________________________________________
Like the Video follow us for more:
Facebook: https://www.facebook.com/altencalsoftlabs
Twitter: https://twitter.com/altencalsoftlab
LinkedIn: https://www.linkedin.com/company/calsoft-labs-india-p-ltd-an-alten-group-company
Google+: https://plus.google.com/+Altencalsoftlabs/
_________________________________________________________________
Looking for similar IT Services?
Write to us Business@Altencalsoftlabs.com
(OR)
Visit Us @ http://www.altencalsoftlabs.com/

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find ...

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find container which is already full. The result is a huge amount of waste buried or burned where it could have been recycled.
A contextual predictive model was developed in order to provide the citizens with the information: what is the best moment to go to which recycling center in terms of waiting time and bin availability ?
This predictive model depends on sensors deployed in each recycling centers and various open data sources.
The API here is the web that links avery parts:
- the sensors to push new version of the software and source the measurements in real time
- the predictive models is fed with fresh measurements and fresh data
- the web/mobile app with the predictions
- the users demands are crowdsourced to a server
- the BI tools of the waste managements authorities
We hope to demonstrate how a predictive API like ours can solve real life problems.
### The slides for this presentation can be found at http://lanyrd.com/2014/papis2014/sdfyrz/
### For more content like this, sign up to the PAPIs.io newsletter at http://papis.io/#updates

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find container which is already full. The result is a huge amount of waste buried or burned where it could have been recycled.
A contextual predictive model was developed in order to provide the citizens with the information: what is the best moment to go to which recycling center in terms of waiting time and bin availability ?
This predictive model depends on sensors deployed in each recycling centers and various open data sources.
The API here is the web that links avery parts:
- the sensors to push new version of the software and source the measurements in real time
- the predictive models is fed with fresh measurements and fresh data
- the web/mobile app with the predictions
- the users demands are crowdsourced to a server
- the BI tools of the waste managements authorities
We hope to demonstrate how a predictive API like ours can solve real life problems.
### The slides for this presentation can be found at http://lanyrd.com/2014/papis2014/sdfyrz/
### For more content like this, sign up to the PAPIs.io newsletter at http://papis.io/#updates

Matti Aksela, VP of analytics at Comptel Corporation, walks through Comptel's new concept of Contextual Intelligence for Telecommunications (CIQ4T) that helps take customer experience management to the next level and enables CSPs to improve their business performance. Utilising advanced predictive analytics, Matti explains the company's innovative approach for allowing CSPs to better determine customers' needs, wants, likes and dislikes at a granular level based on historical and real-time data and predictive modelling.

Matti Aksela, VP of analytics at Comptel Corporation, walks through Comptel's new concept of Contextual Intelligence for Telecommunications (CIQ4T) that helps take customer experience management to the next level and enables CSPs to improve their business performance. Utilising advanced predictive analytics, Matti explains the company's innovative approach for allowing CSPs to better determine customers' needs, wants, likes and dislikes at a granular level based on historical and real-time data and predictive modelling.

Grokr (contextual predictive search app) on iPad mini

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみま...

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings ...

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

Contextual Intelligence - predictive reminders

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventio...

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

published: 23 Mar 2013

Lars Muckli - 2016 CCN Workshop: Predictive Coding

Center for Cognitive Neuroscience at Dartmouth
2016 Workshop: Predictive Coding
LARS MUCKLI, UNIVERSITY OF GLASGOW
Visual predictions in different layers of visual cortex
Abstract:
Our brain imaging research has contributed to what is now seen as a paradigm shift in cognitive Neuroscience. Many agree that the brain can be conceptualized as a prediction machine; internal models predict future states, which are then compared to the incoming stream of sensory information. This new conceptual framework opens a number of essential empirical questions: How are predictions communicated? How precise are top-down projected predictions? How are prediction-errors signalled upstream and how are they used to update internal models? We have pioneered several empirical approaches, the most recent one u...

published: 02 Sep 2016

Playtime 2016 - Predicting lifetime value in the apps world

Deepdive into lifetime value models and predictive analytics in the apps ecosystem. Tactics to get the most out of identified segments and how to upgrade their behaviours to minimise churn.

PyData Berlin 2016
The development of large-scale Knowledge Base (KB) has drawn lots of attentions and efforts from both academy and industries recently . In this talk I will introduce how to use keywords and public available data to build our structural KB, and build knowledge retrieval system for different languages using python.
Many large-scale Knowledge Bases (KB), such as Yago, Wikidata, Freebase, and Google’s Knowledge Graph, have been build by extracting facts fro structural Wikipedia and/or natural language Web documents.
The main observation of using knowledge base is that not all facts are useful and have enough information. To tackle this problem I will introduce how we build various data sources to help facts and keywords selection. We will also discuss important questions ...

Contextual Recommendations in Multi-User Devices
Lecture by Technion alumnus RonnyLempel, ChiefDataScientist, Yahoo! Labs - Technion Computer EngineeringCenter, March 27, 2014
Recommendation technology is often applied on experiences consumed through a personal device such as a smartphone or a laptop, or through personal accounts such as one's social network account. However, in other cases, recommendation technology is applied in settings where multiple users share the application or device. Examples include game consoles or high end smart TVs in household livingrooms, family accounts in VOD subscription services, and shared desktops in homes. These multi-user cases represent a challenge to recommender systems, as recommendations exposed to one user may actually be more suitable for a...

Forrester's Shar VanBoskirk provides cases and insight on how marketers are taking what they've learned from one market and strengthening their context-based marketing in another.

published: 29 Jan 2015

2017 Personality 21: Biology & Traits: Performance Prediction

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of such prediction, in the business and other environments; about the economic value of accurate prediction (in hiring, placement and promotion) -- which is incredibly high.
Intelligence (psychometrically measured IQ) is the best predictor of performance in complex, ever changing environments. Conscientiousness is the (next) best predictor, particularly in the military, in school and in conservative businesses. Agreeable people make better caretakers; disagreeable people, better disciplinarians and negotiators (within reasonable bounds). Open people are artistic, creative and entrepreneurial. Extraverts are good socially. Introverts...

Author:
Meng Jiang, Department of Computer Science, University of Illinois at Urbana-ChampaignAbstract:
Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensor-based summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this pap...

published: 10 Oct 2016

Understanding Location Contextual Intelligence

published: 14 Oct 2015

Contextual Processing in PTSD

Webinar Summary
The brain mechanisms that underlie PTSD are not yet understood. Fear condition and extinction models have been originally proposed, and broadly accepted as candidate mechanisms for PTSD development, however more recently the limitations of these models gained increasing attention. We had proposed that deficits in the processing of contextual information are at the core of PTSD pathophysiology, involving complex interplay between fear learning, memory, sleep, arousal regulation and stress responses in PTSD. We conducted functional neuroimaging studies in PTSD subjects as well as translational studies in animal model of PTSD, to identify brain regions, as well as physiological and molecular mechanisms involved in contextual processing deficits. Here we explore the converging...

Learn how to improve your CaseManagement with Content Analytics. Benefit from new business value with contextual search, investigative analytics, predictive analytics, and more!
Want more about Content Analytics? Check out this FREE report: http://info.aiim.org/using-analytics-automating-processes-and-extracting-knowledge

Predictive Search

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings ...

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

Center for Cognitive Neuroscience at Dartmouth
2016 Workshop: Predictive Coding
LARS MUCKLI, UNIVERSITY OF GLASGOW
Visual predictions in different layers of visual cortex
Abstract:
Our brain imaging research has contributed to what is now seen as a paradigm shift in cognitive Neuroscience. Many agree that the brain can be conceptualized as a prediction machine; internal models predict future states, which are then compared to the incoming stream of sensory information. This new conceptual framework opens a number of essential empirical questions: How are predictions communicated? How precise are top-down projected predictions? How are prediction-errors signalled upstream and how are they used to update internal models? We have pioneered several empirical approaches, the most recent one utilizing ultra-high field fMRI, to investigate layer specific information content in cortical feedback (Muckli et al., 2015, Curr Biol). We use paradigms in which direct feedforward inputs to retinotopic visual areas are occluded (Muckli & Petro 2013 Curr Opin Neurobiol), including visual illusions (apparent motion, Alink et al. 2010, JNS; Petro & Muckli 2016, PNAS comment), auditory contextual scene stimulation in blindfolded subjects (Vetter et al. 2014 Curr Biol), and variations on our occlusion paradigm (Smith & Muckli 2010, PNAS) to uncover contextual feedback information to superficial layers of primary visual cortex. These paradigms allow us to measure spatial precision of feedback, temporal unfolding of feedback during saccadic eye-movements (Edwards et al., under review, Curr Biol), and other abstract categorical and task-dependent feedback information.
We are extending our framework to reconstruct and visualize cortical feedback – an approach that can be conceptualized as a day-dream reader: i.e. visualizing the internal models during mental imagery. We are planning extensions into long-term temporal predictions and mental time travel. In collaboration with rodent research labs, we are investigating the dendritic contribution to the superficial layers processing. Research on predictive processing affects brain-scale simulations (HBP), and conceptual and philosophical collaborations (Andy Clark, Jacob Hohwy).

Center for Cognitive Neuroscience at Dartmouth
2016 Workshop: Predictive Coding
LARS MUCKLI, UNIVERSITY OF GLASGOW
Visual predictions in different layers of visual cortex
Abstract:
Our brain imaging research has contributed to what is now seen as a paradigm shift in cognitive Neuroscience. Many agree that the brain can be conceptualized as a prediction machine; internal models predict future states, which are then compared to the incoming stream of sensory information. This new conceptual framework opens a number of essential empirical questions: How are predictions communicated? How precise are top-down projected predictions? How are prediction-errors signalled upstream and how are they used to update internal models? We have pioneered several empirical approaches, the most recent one utilizing ultra-high field fMRI, to investigate layer specific information content in cortical feedback (Muckli et al., 2015, Curr Biol). We use paradigms in which direct feedforward inputs to retinotopic visual areas are occluded (Muckli & Petro 2013 Curr Opin Neurobiol), including visual illusions (apparent motion, Alink et al. 2010, JNS; Petro & Muckli 2016, PNAS comment), auditory contextual scene stimulation in blindfolded subjects (Vetter et al. 2014 Curr Biol), and variations on our occlusion paradigm (Smith & Muckli 2010, PNAS) to uncover contextual feedback information to superficial layers of primary visual cortex. These paradigms allow us to measure spatial precision of feedback, temporal unfolding of feedback during saccadic eye-movements (Edwards et al., under review, Curr Biol), and other abstract categorical and task-dependent feedback information.
We are extending our framework to reconstruct and visualize cortical feedback – an approach that can be conceptualized as a day-dream reader: i.e. visualizing the internal models during mental imagery. We are planning extensions into long-term temporal predictions and mental time travel. In collaboration with rodent research labs, we are investigating the dendritic contribution to the superficial layers processing. Research on predictive processing affects brain-scale simulations (HBP), and conceptual and philosophical collaborations (Andy Clark, Jacob Hohwy).

PyData Berlin 2016
The development of large-scale Knowledge Base (KB) has drawn lots of attentions and efforts from both academy and industries recently . In t...

PyData Berlin 2016
The development of large-scale Knowledge Base (KB) has drawn lots of attentions and efforts from both academy and industries recently . In this talk I will introduce how to use keywords and public available data to build our structural KB, and build knowledge retrieval system for different languages using python.
Many large-scale Knowledge Bases (KB), such as Yago, Wikidata, Freebase, and Google’s Knowledge Graph, have been build by extracting facts fro structural Wikipedia and/or natural language Web documents.
The main observation of using knowledge base is that not all facts are useful and have enough information. To tackle this problem I will introduce how we build various data sources to help facts and keywords selection. We will also discuss important questions of KB applications including, - architecture of a KB processing and extraction system using Wikipedia and two public available KB including Wikidata and Yago; - method for calculating contextual relevance between facts. - how to present different facts to users.
Yago: https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/
Wikidata: https://www.wikidata.org/wiki/Wikidata:Main_Page
Freebase: https://developers.google.com/freebase/
Google’s Knowledge Graph: https://developers.google.com/knowledge-graph/

PyData Berlin 2016
The development of large-scale Knowledge Base (KB) has drawn lots of attentions and efforts from both academy and industries recently . In this talk I will introduce how to use keywords and public available data to build our structural KB, and build knowledge retrieval system for different languages using python.
Many large-scale Knowledge Bases (KB), such as Yago, Wikidata, Freebase, and Google’s Knowledge Graph, have been build by extracting facts fro structural Wikipedia and/or natural language Web documents.
The main observation of using knowledge base is that not all facts are useful and have enough information. To tackle this problem I will introduce how we build various data sources to help facts and keywords selection. We will also discuss important questions of KB applications including, - architecture of a KB processing and extraction system using Wikipedia and two public available KB including Wikidata and Yago; - method for calculating contextual relevance between facts. - how to present different facts to users.
Yago: https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/
Wikidata: https://www.wikidata.org/wiki/Wikidata:Main_Page
Freebase: https://developers.google.com/freebase/
Google’s Knowledge Graph: https://developers.google.com/knowledge-graph/

Contextual Recommendations in Multi-User Devices
Lecture by Technion alumnus RonnyLempel, ChiefDataScientist, Yahoo! Labs - Technion Computer EngineeringCenter, March 27, 2014
Recommendation technology is often applied on experiences consumed through a personal device such as a smartphone or a laptop, or through personal accounts such as one's social network account. However, in other cases, recommendation technology is applied in settings where multiple users share the application or device. Examples include game consoles or high end smart TVs in household livingrooms, family accounts in VOD subscription services, and shared desktops in homes. These multi-user cases represent a challenge to recommender systems, as recommendations exposed to one user may actually be more suitable for another user.
This talk tackles the shared device recommendation problem by applying context to implicitly disambiguate the user (or users) that are being recommended to. Specifically, we address the household smart-TV situation and introduce the WatchItNext problem, which — given a device — taps the currently watched show as well as the time of day as context for recommending what to watch next. Implicitly, the context serves to disambiguate the current viewers of the device and enables the algorithm to recommend significantly more relevant watching options than those output by state-of-the-art non-contextual recommenders. Our experiments, which processed 4-months long viewing histories of over 350,000 devices, validate the importance and effectiveness of contextual recommendation in shared device settings.
Joint work with Raz Nissim, Michal Aharon, Eshcar Hillel, Amit Kagian and Hayim Makabee.

Contextual Recommendations in Multi-User Devices
Lecture by Technion alumnus RonnyLempel, ChiefDataScientist, Yahoo! Labs - Technion Computer EngineeringCenter, March 27, 2014
Recommendation technology is often applied on experiences consumed through a personal device such as a smartphone or a laptop, or through personal accounts such as one's social network account. However, in other cases, recommendation technology is applied in settings where multiple users share the application or device. Examples include game consoles or high end smart TVs in household livingrooms, family accounts in VOD subscription services, and shared desktops in homes. These multi-user cases represent a challenge to recommender systems, as recommendations exposed to one user may actually be more suitable for another user.
This talk tackles the shared device recommendation problem by applying context to implicitly disambiguate the user (or users) that are being recommended to. Specifically, we address the household smart-TV situation and introduce the WatchItNext problem, which — given a device — taps the currently watched show as well as the time of day as context for recommending what to watch next. Implicitly, the context serves to disambiguate the current viewers of the device and enables the algorithm to recommend significantly more relevant watching options than those output by state-of-the-art non-contextual recommenders. Our experiments, which processed 4-months long viewing histories of over 350,000 devices, validate the importance and effectiveness of contextual recommendation in shared device settings.
Joint work with Raz Nissim, Michal Aharon, Eshcar Hillel, Amit Kagian and Hayim Makabee.

2017 Personality 21: Biology & Traits: Performance Prediction

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of ...

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of such prediction, in the business and other environments; about the economic value of accurate prediction (in hiring, placement and promotion) -- which is incredibly high.
Intelligence (psychometrically measured IQ) is the best predictor of performance in complex, ever changing environments. Conscientiousness is the (next) best predictor, particularly in the military, in school and in conservative businesses. Agreeable people make better caretakers; disagreeable people, better disciplinarians and negotiators (within reasonable bounds). Open people are artistic, creative and entrepreneurial. Extraverts are good socially. Introverts work well in isolation. People low in neuroticism have higher levels of tolerance for stress (but may be less sensitive to real signs of danger).
Match the career you pursue to your temperament, rather than trying to adjust the latter. Although some adjustment is possible, there are powerful biological determinants of the five personality dimensions and IQ (particularly in environments where differences are allowed to flourish).
To support this channel: Patreon: https://www.patreon.com/jordanbpeterson
Other relevant links:
Personality analysis: www.understandmyself.com
Self Authoring: http://selfauthoring.com/
Jordan Peterson Website: http://jordanbpeterson.com/
Podcast: http://jordanbpeterson.com/jordan-b-p...ReadingList: http://jordanbpeterson.com/2017/03/gr...
Twitter: https://twitter.com/jordanbpeterson

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of such prediction, in the business and other environments; about the economic value of accurate prediction (in hiring, placement and promotion) -- which is incredibly high.
Intelligence (psychometrically measured IQ) is the best predictor of performance in complex, ever changing environments. Conscientiousness is the (next) best predictor, particularly in the military, in school and in conservative businesses. Agreeable people make better caretakers; disagreeable people, better disciplinarians and negotiators (within reasonable bounds). Open people are artistic, creative and entrepreneurial. Extraverts are good socially. Introverts work well in isolation. People low in neuroticism have higher levels of tolerance for stress (but may be less sensitive to real signs of danger).
Match the career you pursue to your temperament, rather than trying to adjust the latter. Although some adjustment is possible, there are powerful biological determinants of the five personality dimensions and IQ (particularly in environments where differences are allowed to flourish).
To support this channel: Patreon: https://www.patreon.com/jordanbpeterson
Other relevant links:
Personality analysis: www.understandmyself.com
Self Authoring: http://selfauthoring.com/
Jordan Peterson Website: http://jordanbpeterson.com/
Podcast: http://jordanbpeterson.com/jordan-b-p...ReadingList: http://jordanbpeterson.com/2017/03/gr...
Twitter: https://twitter.com/jordanbpeterson

Author:
Meng Jiang, Department of Computer Science, University of Illinois at Urbana-ChampaignAbstract:
Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensor-based summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional-values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice. We further develop a propagation method CATCHTAR-TAN to catch the dynamic multicontextual patterns from the temporal multidimensional data in a principled and scalable way: it determines the meaningfulness of updating every element in the Tartan by minimizing the encoding cost in a compression manner. CATCHTARTAN outperforms the baselines on both the accuracy and speed. We apply CATCHTARTAN to four Twitter datasets up to 10 million tweets and the DBLP data, providing comprehensive summaries for the events, human life and scientific development.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/

Author:
Meng Jiang, Department of Computer Science, University of Illinois at Urbana-ChampaignAbstract:
Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensor-based summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional-values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice. We further develop a propagation method CATCHTAR-TAN to catch the dynamic multicontextual patterns from the temporal multidimensional data in a principled and scalable way: it determines the meaningfulness of updating every element in the Tartan by minimizing the encoding cost in a compression manner. CATCHTARTAN outperforms the baselines on both the accuracy and speed. We apply CATCHTARTAN to four Twitter datasets up to 10 million tweets and the DBLP data, providing comprehensive summaries for the events, human life and scientific development.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/

Contextual Processing in PTSD

Webinar Summary
The brain mechanisms that underlie PTSD are not yet understood. Fear condition and extinction models have been originally proposed, and broadly...

Webinar Summary
The brain mechanisms that underlie PTSD are not yet understood. Fear condition and extinction models have been originally proposed, and broadly accepted as candidate mechanisms for PTSD development, however more recently the limitations of these models gained increasing attention. We had proposed that deficits in the processing of contextual information are at the core of PTSD pathophysiology, involving complex interplay between fear learning, memory, sleep, arousal regulation and stress responses in PTSD. We conducted functional neuroimaging studies in PTSD subjects as well as translational studies in animal model of PTSD, to identify brain regions, as well as physiological and molecular mechanisms involved in contextual processing deficits. Here we explore the converging evidence of abnormalities in hippocampal-prefrontal-thalamic circuits in PTSD patients and animal models, further dissecting candidate molecular mechanisms involved. Together these studies transform our understanding of PTSD pathophysiology suggesting a more complex and nuanced model of pathophysiologic processes involved, understanding that can lead to novel and improved future treatments.
About IsraelLiberzon, MD
Israel Liberzon, MD, is a Professor of Psychiatry, Psychology, and Neuroscience and Director of the Psychiatric ResidencyResearchTrack in the Department of Psychiatry at the University of Michigan. After graduating from Sacklers Medical School, Tel Aviv University, Dr. Liberzon completed his post-doctoral training in physiology at Rappaport Institute, Israeli Institute of Technology in Haifa. He completed his Psychiatry Residence Program at the University of Michigan, and, since 1992, has been faculty in the University of Michigan’s Department of Psychiatry, Department of Psychology and Neuroscience program.
Dr. Liberzon’s primary research interest centers on emotions, stress, and stress-related disorders like PTSD, particularly in the regulation and dysregulation of stress response systems. In 1992, he established the PTSD program at the University of Michigan and Ann ArborVA Medical Center, a program that has since grown and remains on the forefront of biological research of PTSD worldwide. He also co-founded the Trauma, Stress, and Anxiety Research Group (TSARG) at the University of Michigan, which includes the Psychiatric Affective Neuroimaging Laboratory, a basic science (wet bench) laboratory, a MiRRR genetic repository, and a clinical research group.
He has published over 200 peer reviewed manuscripts, many of them in the leading journals like Nature Reviews Neuroscience, PNAS, Neuron, JAMA Psychiatry, Journal of Neuroscience, NEJM and more, and has authored and edited several book chapters and reviews including the upcoming book: The Neurobiology of PTSD. He serves on NIH and VA study sections, served as a reviewer for Institute of Medicine, and Department of Defense Congressional reports as well as various international funding agencies.

Webinar Summary
The brain mechanisms that underlie PTSD are not yet understood. Fear condition and extinction models have been originally proposed, and broadly accepted as candidate mechanisms for PTSD development, however more recently the limitations of these models gained increasing attention. We had proposed that deficits in the processing of contextual information are at the core of PTSD pathophysiology, involving complex interplay between fear learning, memory, sleep, arousal regulation and stress responses in PTSD. We conducted functional neuroimaging studies in PTSD subjects as well as translational studies in animal model of PTSD, to identify brain regions, as well as physiological and molecular mechanisms involved in contextual processing deficits. Here we explore the converging evidence of abnormalities in hippocampal-prefrontal-thalamic circuits in PTSD patients and animal models, further dissecting candidate molecular mechanisms involved. Together these studies transform our understanding of PTSD pathophysiology suggesting a more complex and nuanced model of pathophysiologic processes involved, understanding that can lead to novel and improved future treatments.
About IsraelLiberzon, MD
Israel Liberzon, MD, is a Professor of Psychiatry, Psychology, and Neuroscience and Director of the Psychiatric ResidencyResearchTrack in the Department of Psychiatry at the University of Michigan. After graduating from Sacklers Medical School, Tel Aviv University, Dr. Liberzon completed his post-doctoral training in physiology at Rappaport Institute, Israeli Institute of Technology in Haifa. He completed his Psychiatry Residence Program at the University of Michigan, and, since 1992, has been faculty in the University of Michigan’s Department of Psychiatry, Department of Psychology and Neuroscience program.
Dr. Liberzon’s primary research interest centers on emotions, stress, and stress-related disorders like PTSD, particularly in the regulation and dysregulation of stress response systems. In 1992, he established the PTSD program at the University of Michigan and Ann ArborVA Medical Center, a program that has since grown and remains on the forefront of biological research of PTSD worldwide. He also co-founded the Trauma, Stress, and Anxiety Research Group (TSARG) at the University of Michigan, which includes the Psychiatric Affective Neuroimaging Laboratory, a basic science (wet bench) laboratory, a MiRRR genetic repository, and a clinical research group.
He has published over 200 peer reviewed manuscripts, many of them in the leading journals like Nature Reviews Neuroscience, PNAS, Neuron, JAMA Psychiatry, Journal of Neuroscience, NEJM and more, and has authored and edited several book chapters and reviews including the upcoming book: The Neurobiology of PTSD. He serves on NIH and VA study sections, served as a reviewer for Institute of Medicine, and Department of Defense Congressional reports as well as various international funding agencies.

Learn how to improve your CaseManagement with Content Analytics. Benefit from new business value with contextual search, investigative analytics, predictive an...

Learn how to improve your CaseManagement with Content Analytics. Benefit from new business value with contextual search, investigative analytics, predictive analytics, and more!
Want more about Content Analytics? Check out this FREE report: http://info.aiim.org/using-analytics-automating-processes-and-extracting-knowledge

Learn how to improve your CaseManagement with Content Analytics. Benefit from new business value with contextual search, investigative analytics, predictive analytics, and more!
Want more about Content Analytics? Check out this FREE report: http://info.aiim.org/using-analytics-automating-processes-and-extracting-knowledge

Contextual Intelligence - predictive reminders

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

3:08

Wysii - Predictive contextual mobile targeting

Wysii by TellMePlus
The predictive, contextual and behavioral targeting platform for mobil...

Grokr (contextual predictive search app) on iPad mini

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

Contextual Anomaly Detection in Big Sensor Data

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.

14:42

Muktabh Mayank - Making a contextual recommendation engine

Making a Recommendation Engine at ParallelDots
a. Why normal full-text search will not wor...

Understanding Consumer Intent & Leveraging Predictive Technologies
Predictive mobile apps are growing in use with a wide variety of productivity and calendaring apps garnering consumer and press attention. What's next for predictive technologies in mobile? How can predictive technologies be used to improve customer experiences and help brands grow? How can companies tap into contextual cues, like location, calendar, email, etc. to better predict and respond to what their consumers want? What other cues can be used to better understand users and respond to their needs? Where are consumers drawing the line between cool and creepy? Join us on the main stage for a conversation with the CEOs of two white hot predictive apps companies - Max Wheeler from Mynd and Mikael Berner from EasilyDo who will be talking to Mark Daiss, the Co-Founder of the intelligent home screen, Aviate (acquired by Yahoo in January 2014) about how predictive technologies can be used to help companies grow.
Panelists: Mikael Berner, CEO & Founder, Easilydo; Max Wheeler, CEO & Founder, Mynd; Mark Daiss, Co-Founder of Aviate, Yahoo
Moderator: Molly Wood, DeputyTechnology Editor, New York TimesWant to learn more about VentureBeat events?
Visit events.venturebeat.com

2:06

Contextual Insights: Making Better Decisions

When you need to make an important decision, you want the information that will help you m...

Contextual Insights: Making Better Decisions

When you need to make an important decision, you want the information that will help you make the best choice. And you want that information in context and at your fingertips. Check out this product spotlight to see how Workday gives you what you need to make better decisions.
Learn more about the EnterpriseCloud for HR and Finance in our ProductPreview at - https://forms.workday.com/uk/landing_page/product_preview_uk_enterprise_cloud_for_hr_and_finance_lp.php

6:01

Why contextual real-time communications is a huge telco opportunity

Almost any app you look at will now have some sort of real-time communication function bui...

Why contextual real-time communications is a huge telco opportunity

Almost any app you look at will now have some sort of real-time communication function built in contextually, says David Walsh, who explains why he thinks this is the biggest opportunity for the carriers to develop services as they transform their infrastructure with NFV - now really taking off - and look to become fast-moving service marketing companies, rather than slow-moving engineering organisations. The old legacy world is changing fast and everything, including Genband’s own software, is being reworked and loaded into the cloud. This will enable telcos to become fast-moving, fast fail, marketing and sales organisations.
Featuring David Walsh, President, CEO and Chairman, GENBAND
FILMED AT: Mobile World Congress 2017, Barcelona

Predictive Analytics in Healthcare

Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning
Objectives:
► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols
► Promotion of wellness and disease management
The Predictors – Predictive Analytics:
In order to predict the re-admission, following data fields/predictors were considered.
► Demographics – Age, Sex
► Lab data – Includes lab tests
► Vitals – Includes BP, Sugar, Weight, etc.
► Visit types – Emergency, In-patient, and Outpatient
► Diagnosis – Diseases/ailments – Heart, Pneumonia
► Previous hospital visit
► Length of stay
► The data was received as a set of .csv files which gave the complete details of Demographics, Admission, vitals, lab tests of selected sample of patients over a period of time.
► The processing of the data included the following activities:
► Removing commas, uploading .csv files to HDFS (Horton works)
► The required DDL scripts were written in Hive
► The necessary joins were written
► The result was refined datasets
► The refined datasets are passed on to Data Analysis team for analysis
The best model is arrived at by testing the data under different classifiers and precision, recall and F1 score metrics calculated for each classifier.
► Gradient Boosting
► Random Forest
► Support Vector Machines
► Logistic Regression
► K-Nearest Neighbor
► Ridge
_________________________________________________________________
Like the Video follow us for more:
Facebook: https://www.facebook.com/altencalsoftlabs
Twitter: https://twitter.com/altencalsoftlab
LinkedIn: https://www.linkedin.com/company/calsoft-labs-india-p-ltd-an-alten-group-company
Google+: https://plus.google.com/+Altencalsoftlabs/
_________________________________________________________________
Looking for similar IT Services?
Write to us Business@Altencalsoftlabs.com
(OR)
Visit Us @ http://www.altencalsoftlabs.com/

19:28

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Recycling centers where designed 40 years a ago and now have trouble managing the huge dem...

Predictive APIs for public needs - Alexandre Vallette - PAPIs.io '14

Recycling centers where designed 40 years a ago and now have trouble managing the huge demand they are facing. Cars often queue for hours and people often find container which is already full. The result is a huge amount of waste buried or burned where it could have been recycled.
A contextual predictive model was developed in order to provide the citizens with the information: what is the best moment to go to which recycling center in terms of waiting time and bin availability ?
This predictive model depends on sensors deployed in each recycling centers and various open data sources.
The API here is the web that links avery parts:
- the sensors to push new version of the software and source the measurements in real time
- the predictive models is fed with fresh measurements and fresh data
- the web/mobile app with the predictions
- the users demands are crowdsourced to a server
- the BI tools of the waste managements authorities
We hope to demonstrate how a predictive API like ours can solve real life problems.
### The slides for this presentation can be found at http://lanyrd.com/2014/papis2014/sdfyrz/
### For more content like this, sign up to the PAPIs.io newsletter at http://papis.io/#updates

Matti Aksela, VP of analytics at Comptel Corporation, walks through Comptel's new concept of Contextual Intelligence for Telecommunications (CIQ4T) that helps take customer experience management to the next level and enables CSPs to improve their business performance. Utilising advanced predictive analytics, Matti explains the company's innovative approach for allowing CSPs to better determine customers' needs, wants, likes and dislikes at a granular level based on historical and real-time data and predictive modelling.

1:59

Grokr (contextual predictive search app) on iPad mini

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's lik...

Grokr (contextual predictive search app) on iPad mini

I took a video of Grokr, a contextual predictive search app running on iPad mini. It's like "Google Now for iOS".
Grokrと言う、コンテクスチャル予想検索エンジンアプリをiPad mini上で試してみました。Grokrは「iOS上で動くGoogle Now」のようなアプリと言われています。

28:22

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Mal...

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

1:20

Contextual Intelligence - predictive reminders

In this video, we see a man whose car has been in an accident. He needs to remember to ta...

Contextual Intelligence - predictive reminders

In this video, we see a man whose car has been in an accident. He needs to remember to take the repair quotes to his insurance agent. Unfortunately, conventional location-based reminder comes too late. He needs a more predictive, proactive reminder. We see a Contextually Intelligent reminder delivered before he leaves the house. The system knows when he is about to leave because he habitually leaves around that same time each Saturday morning.

Meet the guy behind Grokr: a contextual predictive search for iPhone

You might have heard about predictive search and discovery services like Google Now or Maluuba, both of which are on Android. But here meet Grokr, which brings that kind of contextual service to iPhone. It tries to assist your life by studying your context. Who you are. Where you have been. What you like. Try it at http://grokrlabs.com

Lars Muckli - 2016 CCN Workshop: Predictive Coding

Center for Cognitive Neuroscience at Dartmouth
2016 Workshop: Predictive Coding
LARS MUCKLI, UNIVERSITY OF GLASGOW
Visual predictions in different layers of visual cortex
Abstract:
Our brain imaging research has contributed to what is now seen as a paradigm shift in cognitive Neuroscience. Many agree that the brain can be conceptualized as a prediction machine; internal models predict future states, which are then compared to the incoming stream of sensory information. This new conceptual framework opens a number of essential empirical questions: How are predictions communicated? How precise are top-down projected predictions? How are prediction-errors signalled upstream and how are they used to update internal models? We have pioneered several empirical approaches, the most recent one utilizing ultra-high field fMRI, to investigate layer specific information content in cortical feedback (Muckli et al., 2015, Curr Biol). We use paradigms in which direct feedforward inputs to retinotopic visual areas are occluded (Muckli & Petro 2013 Curr Opin Neurobiol), including visual illusions (apparent motion, Alink et al. 2010, JNS; Petro & Muckli 2016, PNAS comment), auditory contextual scene stimulation in blindfolded subjects (Vetter et al. 2014 Curr Biol), and variations on our occlusion paradigm (Smith & Muckli 2010, PNAS) to uncover contextual feedback information to superficial layers of primary visual cortex. These paradigms allow us to measure spatial precision of feedback, temporal unfolding of feedback during saccadic eye-movements (Edwards et al., under review, Curr Biol), and other abstract categorical and task-dependent feedback information.
We are extending our framework to reconstruct and visualize cortical feedback – an approach that can be conceptualized as a day-dream reader: i.e. visualizing the internal models during mental imagery. We are planning extensions into long-term temporal predictions and mental time travel. In collaboration with rodent research labs, we are investigating the dendritic contribution to the superficial layers processing. Research on predictive processing affects brain-scale simulations (HBP), and conceptual and philosophical collaborations (Andy Clark, Jacob Hohwy).

39:14

Playtime 2016 - Predicting lifetime value in the apps world

Deepdive into lifetime value models and predictive analytics in the apps ecosystem. Tactic...

PyData Berlin 2016
The development of large-scale Knowledge Base (KB) has drawn lots of attentions and efforts from both academy and industries recently . In this talk I will introduce how to use keywords and public available data to build our structural KB, and build knowledge retrieval system for different languages using python.
Many large-scale Knowledge Bases (KB), such as Yago, Wikidata, Freebase, and Google’s Knowledge Graph, have been build by extracting facts fro structural Wikipedia and/or natural language Web documents.
The main observation of using knowledge base is that not all facts are useful and have enough information. To tackle this problem I will introduce how we build various data sources to help facts and keywords selection. We will also discuss important questions of KB applications including, - architecture of a KB processing and extraction system using Wikipedia and two public available KB including Wikidata and Yago; - method for calculating contextual relevance between facts. - how to present different facts to users.
Yago: https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/
Wikidata: https://www.wikidata.org/wiki/Wikidata:Main_Page
Freebase: https://developers.google.com/freebase/
Google’s Knowledge Graph: https://developers.google.com/knowledge-graph/

Contextual Recommendations in Multi-User Devices
Lecture by Technion alumnus RonnyLempel, ChiefDataScientist, Yahoo! Labs - Technion Computer EngineeringCenter, March 27, 2014
Recommendation technology is often applied on experiences consumed through a personal device such as a smartphone or a laptop, or through personal accounts such as one's social network account. However, in other cases, recommendation technology is applied in settings where multiple users share the application or device. Examples include game consoles or high end smart TVs in household livingrooms, family accounts in VOD subscription services, and shared desktops in homes. These multi-user cases represent a challenge to recommender systems, as recommendations exposed to one user may actually be more suitable for another user.
This talk tackles the shared device recommendation problem by applying context to implicitly disambiguate the user (or users) that are being recommended to. Specifically, we address the household smart-TV situation and introduce the WatchItNext problem, which — given a device — taps the currently watched show as well as the time of day as context for recommending what to watch next. Implicitly, the context serves to disambiguate the current viewers of the device and enables the algorithm to recommend significantly more relevant watching options than those output by state-of-the-art non-contextual recommenders. Our experiments, which processed 4-months long viewing histories of over 350,000 devices, validate the importance and effectiveness of contextual recommendation in shared device settings.
Joint work with Raz Nissim, Michal Aharon, Eshcar Hillel, Amit Kagian and Hayim Makabee.

2017 Personality 21: Biology & Traits: Performance Prediction

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of such prediction, in the business and other environments; about the economic value of accurate prediction (in hiring, placement and promotion) -- which is incredibly high.
Intelligence (psychometrically measured IQ) is the best predictor of performance in complex, ever changing environments. Conscientiousness is the (next) best predictor, particularly in the military, in school and in conservative businesses. Agreeable people make better caretakers; disagreeable people, better disciplinarians and negotiators (within reasonable bounds). Open people are artistic, creative and entrepreneurial. Extraverts are good socially. Introverts work well in isolation. People low in neuroticism have higher levels of tolerance for stress (but may be less sensitive to real signs of danger).
Match the career you pursue to your temperament, rather than trying to adjust the latter. Although some adjustment is possible, there are powerful biological determinants of the five personality dimensions and IQ (particularly in environments where differences are allowed to flourish).
To support this channel: Patreon: https://www.patreon.com/jordanbpeterson
Other relevant links:
Personality analysis: www.understandmyself.com
Self Authoring: http://selfauthoring.com/
Jordan Peterson Website: http://jordanbpeterson.com/
Podcast: http://jordanbpeterson.com/jordan-b-p...ReadingList: http://jordanbpeterson.com/2017/03/gr...
Twitter: https://twitter.com/jordanbpeterson

Author:
Meng Jiang, Department of Computer Science, University of Illinois at Urbana-ChampaignAbstract:
Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensor-based summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional-values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice. We further develop a propagation method CATCHTAR-TAN to catch the dynamic multicontextual patterns from the temporal multidimensional data in a principled and scalable way: it determines the meaningfulness of updating every element in the Tartan by minimizing the encoding cost in a compression manner. CATCHTARTAN outperforms the baselines on both the accuracy and speed. We apply CATCHTARTAN to four Twitter datasets up to 10 million tweets and the DBLP data, providing comprehensive summaries for the events, human life and scientific development.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/

Contextual Processing in PTSD

Webinar Summary
The brain mechanisms that underlie PTSD are not yet understood. Fear condition and extinction models have been originally proposed, and broadly accepted as candidate mechanisms for PTSD development, however more recently the limitations of these models gained increasing attention. We had proposed that deficits in the processing of contextual information are at the core of PTSD pathophysiology, involving complex interplay between fear learning, memory, sleep, arousal regulation and stress responses in PTSD. We conducted functional neuroimaging studies in PTSD subjects as well as translational studies in animal model of PTSD, to identify brain regions, as well as physiological and molecular mechanisms involved in contextual processing deficits. Here we explore the converging evidence of abnormalities in hippocampal-prefrontal-thalamic circuits in PTSD patients and animal models, further dissecting candidate molecular mechanisms involved. Together these studies transform our understanding of PTSD pathophysiology suggesting a more complex and nuanced model of pathophysiologic processes involved, understanding that can lead to novel and improved future treatments.
About IsraelLiberzon, MD
Israel Liberzon, MD, is a Professor of Psychiatry, Psychology, and Neuroscience and Director of the Psychiatric ResidencyResearchTrack in the Department of Psychiatry at the University of Michigan. After graduating from Sacklers Medical School, Tel Aviv University, Dr. Liberzon completed his post-doctoral training in physiology at Rappaport Institute, Israeli Institute of Technology in Haifa. He completed his Psychiatry Residence Program at the University of Michigan, and, since 1992, has been faculty in the University of Michigan’s Department of Psychiatry, Department of Psychology and Neuroscience program.
Dr. Liberzon’s primary research interest centers on emotions, stress, and stress-related disorders like PTSD, particularly in the regulation and dysregulation of stress response systems. In 1992, he established the PTSD program at the University of Michigan and Ann ArborVA Medical Center, a program that has since grown and remains on the forefront of biological research of PTSD worldwide. He also co-founded the Trauma, Stress, and Anxiety Research Group (TSARG) at the University of Michigan, which includes the Psychiatric Affective Neuroimaging Laboratory, a basic science (wet bench) laboratory, a MiRRR genetic repository, and a clinical research group.
He has published over 200 peer reviewed manuscripts, many of them in the leading journals like Nature Reviews Neuroscience, PNAS, Neuron, JAMA Psychiatry, Journal of Neuroscience, NEJM and more, and has authored and edited several book chapters and reviews including the upcoming book: The Neurobiology of PTSD. He serves on NIH and VA study sections, served as a reviewer for Institute of Medicine, and Department of Defense Congressional reports as well as various international funding agencies.

Learn how to improve your CaseManagement with Content Analytics. Benefit from new business value with contextual search, investigative analytics, predictive analytics, and more!
Want more about Content Analytics? Check out this FREE report: http://info.aiim.org/using-analytics-automating-processes-and-extracting-knowledge

2014 Personality Lecture 21: Performance Predictio...

PRE-TARGETING, PRE-AWARENESS… THE DAWN OF TRUE PRE...

Predictive Search...

It turns out that a theory explaining how we might detect parallel universes and prediction for the end of the world was proposed and completed by physicist Stephen Hawking shortly before he died ... &nbsp;. According to reports, the work predicts that the universe would eventually end when stars run out of energy ... ....

Article by WN.Com Correspondent Dallas DarlingIt wasn’t very long ago Republicans were accusing Democrats of either paying a few dollars to the homeless for votes or giving them a pack of cigarettes. But with Donald Trump, it’s obvious he paid $130,000 to an adult-film star in exchange for her silence last October and just before the general election ... Was the payment from his own account – or from a lawyer – or from campaign donations....

Using e-cigarettes may lead to an accumulation of fat in the liver, a study of mice exposed to the devices suggests. “The popularity of electronic cigarettes has been rapidly increasing in part because of advertisements that they are safer than conventional cigarettes ... Friedman of Charles R. Drew University of Medicine and Science in Los Angeles, California ... Circadian rhythm dysfunction is known to accelerate liver disease....

Prediction? Fans have spotted that Stephen Hawking nods to Albert Einstein an episode of The Simpsons which aired in 1999, 19 years before the physicist would die on the latter German scientist's day of birth ... Fans have already tied together Wednesday 14 March as the date of Hawking's death (left) and the day that Albert Einstein was born (right) but did The Simpsons predict their greater link years ahead of time?....

Food and Drug Administration granted the De Novo request for Edwards' Acumen Hypotension PredictionIndex (HPI) software. The company will initiate a targeted launch of this first-of-its-kind technology that leverages predictive analytics to alert clinicians to address potential hypotension, or low blood pressure, before it occurs in their surgical patients....

Climate Change commissioner RachelAnne S ... However, there is greater burden on women because of societal and cultural responsibilities ... She also mentioned that the CCC would further strengthen partnerships with other stakeholders, especially those in the women and gender advocacy, to further contextualize gender in our climate policies and measures. ....

Since the beginning of recorded history there have been end of the world predictions... The book is a point-by-point takedown of the predictions of disaster made by the climate change movement, none of which have materialized, but when one is part of a cult, facts don’t matter ... “We meteorologists are well aware of how limited our ability is to predict the weather....

In fact, they predict, it's not likely to leave anything white in its wake ...Little or no accumulation of snow is expected, and that will hold true through the day Tuesday when a rain/snow mix is predicted by the federal forecasters The daytime high is forecast at a chilly 38 ... with less than a tenth of an inch of precipitation predicted....

Why? Because they believe Paswan has in the past correctly predicted the direction of electoral winds ...That's precisely what makes him, "India's most accurate political expert, psephologist with (the) best prediction track record", said many on the microblogging site. Ram Vilas Paswan is India's most accurate political expert, psephologist with best prediction track record......

You may have heard of Numerai — the unorthodox hedge fund that crowdsources predictive stock market models from data scientists around the world ...The San Francisco-based hedge fund incentivizes its community members by giving them digital tokens they can stake during tournaments to express confidence in their predictions....

The Alphacat platform will consist of robots that utilize both artificial intelligence (AI) and ‘big data’ to predict the future price of specific cryptocurrencies ... Alphacat’s AI robots use big data to calculate the likelihood that a particular market will increase, and to date, its price prediction for Bitcoin has an accuracy of 60%. There is no other prediction platform on the market with this level of accuracy....

The Log Cabin Democrat reports that DNA samples were sent off to ParabonNanoLabs, a DNA technology company that specialized in DNA phenotyping, which is the process of predicting physical appearance and ancestry from unidentified DNA evidence... "Using DNA evidence from this investigation, Snapshot produced trait predictions for a person of interest," Bledsoe said....

LAHORE. Partly cloudy weather was observed in the City on Sunday while Met Officepredicted similar weather conditions for the next 24 hours. Met officials said a westerly wave was present along western parts of the country and it might affect the central and upper parts of the country from Monday to Wednesday. They predicted that partly cloudy conditions were expected in most western parts of the country ... ....